Virtual manager

ABSTRACT

A virtual management system comprises video cameras, and various other sensors that acquire event data indicative relating to the processing of stock. This data is passed to a local data collection device that aggregates the event data and passes it via a network to a number of remote data processing modules. The event data is allocated to each of the data processing modules based upon their assigned tasks by a virtual manager agent. A data processing module receives the aggregated event data from the local data collection device via a network and processes the event data according to a set of pre-defined rules. The data processing module generates an alert in response to the processing of the event data indicating that a pre-defined event has occurred, and transmits the alert to a remote device associated with an employee.

TECHNICAL FIELD

The present invention relates to a virtual manager. More specifically,but not exclusively, it relates to a retail virtual manager. Even morespecifically, but not exclusively, it relates to an intelligent remoteretail virtual manager.

BACKGROUND ART

Retailers encounter a large number of factors which server to reducetheir profitability, in particular their gross margin. One non-limitingexample of factors which adversely affect profitability, include“sweethearting” where a customer pays for a low value item whilstpurchasing a high value item with the collusion of the checkoutassistant, or at a self-service checkout terminal. Another non-limitingexample is where the layout of a retail unit is such that it is notcustomer friendly, resulting in poor sales of stock items that wouldotherwise realise higher sales in an alternative position within theretail unit.

In order to address these issues it is common practice to employ a teamof managers to cover all opening hours of a retail unit. This practiceis expensive for the retail unit owner and does not address the casewhere one, or more, of the team of managers is indulging in thefraudulent activity and is therefore not likely to address thefraudulent activity.

Attempts have been made to correlate point-of-sale (POS) terminaloutputs with video surveillance footage in order to identify fraudulentactivity such as sweethearting, see for example U.S. Pat. No. 7,631,808B (STOPLIFT, INC). However, these POS-video correlations merely identifyfraudulent activity and do not add further value to the retailer, thereis no attempt to further increase the gross margin associated with aretail unit by identifying further issues with, for example the retailunit's layout.

Additionally, the prior art solutions identify that a problem hasoccurred but do not automatically identify the reoccurrence of problemthat may be indicative of a failure of a manager to address theissue(s).

Naturally, the cost and complexity of addressing gross margin issuesincreases with the estate of the retailer, for example a large retailermay divide their estate into regions under regional managers reportingin to an overall manager who reports to the chief executive. The presentattempts to identify fraudulent transactions do not address how toescalate notification of problems to the appropriate person withinretailer, for example the escalation from a regional manager to ageneral manager if a problem is seen to be recurrent within a particularregion, but not in other regions. Thus, in prior art systems there is nocorrelation between the nature and occurrence of an issue and itsescalation through the retailer's organisational hierarchy.

DISCLOSURE OF INVENTION

According to a first aspect of the present invention there is provided avirtual management system comprising: at least one data acquisitiondevice arranged to acquire event data indicative of an event at alocation; a local data collection device arranged to aggregate the eventdata; at least one data processing module arranged to receive theaggregated event data from the local data collection device via anetwork, and being further arranged to process the event data accordingto a set of pre-defined rules; the at least one data processing modulebeing further arranged to generate an alert in response to theprocessing of the event data indicating that a predefined event hasoccurred, and being further arranged to transmit the alert to a remotedevice associated with an employee.

The rules may comprise at least one escalation rule related to theescalation of the alert through a management hierarchy. The at least oneescalation rule may be related to escalating the alert based upon atleast one of the following: a delay in entering a response to the alertat a, or the, remote device, an increased frequency of the event, thereoccurrence of the event. The at least one escalation rule may berelated to at least one of the following: a particular retail unit, areawithin a retail unit, a group of retail units, a geographical area, aperson, group of persons, a relationship between persons, a time period.

The rules may be arranged to identify clusters of events. The clustersmay be geographically linked, temporally linked, technologically linked,and/or linked to one or more persons, and/or one or more businessfactors. Business factors may include, by way of non-limiting exampleonly, footfall, spend per customer, customer satisfaction, type of goodspurchased.

The rules may comprise dynamically variable rules. The dynamicallyvariable rules may comprise machine-learning algorithms.

The at least one data processing module may be arranged to compare anevent identified by the rules to stored model event data. The at leastone processing module may be arranged to update parameters associatedwith the model event data in response to the comparison. The at leastone processing module may be arranged to selectively generate the alertbased upon the comparison.

The at least one data acquisition device comprises at least one of thefollowing: a POS terminal, a video camera, a radio-frequencyidentification (RFID) tag, an electronic price label (EPL), a locationsensor, an audio sensor, an accelerometer, a magnetometer, anelectrometer, an electro-optical sensor, a tactile sensor, apiezoelectric sensor, a heat sensor, a proximity sensor, any othersuitable sensor for detecting any of the following: position, speed,heat, presence of an object, position, angle, distance, displacement,electrical field, electromagnetic field, gravitational field, force,density, direction, flow properties, for example but not limited to,flow of people, chemical sensor, environmental sensor, for example butnot limited to weather sensor.

The at least one data processing module may comprise a rule arranged toidentify a correlation between POS physical event data and video eventdata which corresponds to an indication of a fraudulent transaction. Theat least one data processing module may comprise a rule arranged toidentify a correlation between POS physical event data, video event dataand input from at least one sensor and/or other input device, whichcorresponds to an indication of a fraudulent transaction. Physical eventdata may include, by way of non-limiting example only, POS transactiondata.

The system may comprise a plurality of data processing modules. Theplurality of data processing modules may be distributed geographically.Differing data processing modules may be arranged to process differentportions of the aggregated event data. Typically, the differing portionsof the event data may relate to differing event types.

Each location has an instance of a virtual manager agent associated withit, the virtual manager agent being arranged to control the applicationof the rules and the generation of the alert. The virtual manager agentmay be arranged to control the escalation of the alert through themanagement hierarchy. The virtual manager agent may be run on the atleast one data processing module, or it may be instantiated across aplurality of the data processing modules.

According to a second aspect of the present invention there is provideda virtual management system data processing unit comprising: atransceiver arranged to control the flow of data to and from the dataprocessing unit; a processor arranged to receive, via the transceiver atleast a portion of event data acquired from at least one dataacquisition device; the processor being further arranged to process theevent data according to a set of pre-defined rules, being arranged togenerate an alert in response to the processing of the event dataindicating that a predefined event has occurred, and being furtherarranged to transmit the alert to a remote device associated with anemployee, via the transceiver.

The rules may be stored locally at a storage device of the processingunit.

The rules may comprise at least one escalation rule related to theescalation of the alert through a management hierarchy. The at least oneescalation rule may be related to escalating the alert based upon atleast one of the following: a delay in entering a response to the alertat a, or the, remote device, an increased frequency of the event, thereoccurrence of the event. The at least one escalation rule may berelated to at least one of the following: a particular retail unit, agroup of retail units, a geographical area, a person, a time period.

The rules may be arranged to identify clusters of events. The clustersmay be geographically linked, temporally linked and/or linked to one ormore persons.

The rules may comprise dynamically variable rules. The dynamicallyvariable rules may comprise machine-learning algorithms. The processormay be arranged to update the rules in response to event data.

The processor may be arranged to compare an event identified by therules to stored model event data. The processor may be arranged toupdate parameters associated with the model event data in response tothe comparison. The processor may be arranged to selectively generatethe alert based upon the comparison.

The processor may have an instance of a virtual manager agent associatedwith a retail store running thereupon, the virtual manager agent beingarranged to control the application of the rules and the generation ofthe alert. The virtual manager agent may be arranged to control theescalation of the alert through a management hierarchy. The virtualmanager agent may be run on the at least one data processing module, orit may be instantiated across a plurality of the data processingmodules.

According to a third aspect of the present invention there is provided amethod of managing a retail store virtually comprising: acquiring eventdata indicative of an event within the retail store at at least one dataacquisition device; receiving aggregated event data from a datacollection device at a data processing module via a network; processingthe event data according to a set of pre-defined rules; generating analert in response to the processing of the event data indicating that apredefined event has occurred at the at least one data processingmodule; and transmitting the alert to a remote device associated with anemployee.

The method may further comprise escalating of the alert through amanagement hierarchy. The method may further comprise escalating thealert based upon at least one of the following: a delay in entering aresponse to the alert at a, or the, remote device, an increasedfrequency of the event, the reoccurrence of the event. The method mayfurther comprise escalating the alert based upon at least one of thefollowing: a particular retail unit, a group of retail units, ageographical area, a person, a time period.

The method may comprise identifying clusters of events. The clusters maybe geographically linked, temporally linked and/or linked to one or morepersons.

The method may comprise varying the rules dynamically. The dynamicallyvariable rules may comprise machine-learning algorithms.

The method may comprise comparing an event identified by the rules tostored model event data. The method may comprise updating parametersassociated with the model event data in response to the comparison. Themethod may comprise selectively generating the alert based upon thecomparison.

The method may comprise identifying a correlation between POS event dataand video event data that corresponds to an indication of a fraudulenttransaction. The method may comprise identifying a correlation betweenPOS event data, video event data and input from at least one sensorand/or other input device that corresponds to an indication of afraudulent transaction.

The method may comprise instantiating an instance of a virtual manageragent associated with a particular retail store, the virtual manageragent being arranged to control the application of the rules and thegeneration of the alert. The method may comprise controlling theescalation of the alert through a management hierarchy via the virtualmanager agent. The method may comprise instantiating a portion a virtualmanager agent associated with a particular retail store on the at leastone data processing module.

According to a fourth aspect of the present invention there is providedsoftware, which when executed upon a processor, causes the processor toact as the processor of the processing unit of the second aspect of thepresent invention.

According to a fifth aspect of the present invention there is provided aretail manager agent which, when executed upon a processor, causes theprocessor to act as the retail management unit of any one of the first,second or third aspects of the present invention.

BRIEF DESCRIPTION OF DRAWINGS

The invention will now be described, by way of example only, withreference to the accompanying drawings, in which:

FIG. 1 is a schematic diagram of an embodiment of virtual retailmanagement system in accordance with at least one aspect of the presentinvention;

FIG. 2 is a schematic diagram of a store deploying the virtual retailmanagement system of FIG. 1; and

FIG. 3 is a flow chart detailing an embodiment of a method of managing aretail store virtually in accordance with another aspect of the presentinvention.

DETAILED DESCRIPTION

Referring now to FIGS. 1 and 2, a virtual retail management system 100comprises a plurality of stores 102 a-d each having a respective virtualmanager agent 104 a-d associated with it, a network 106, a number ofdata analysis modules 108 a-c and a number of remote terminals 110 a-dassociated with employees of a retailer who owns the stores 102 a-d.Typically, a remote terminal 110 a-d will comprise a mobile telephone, atablet, a PC or a laptop.

Each of the stores 102 a-d comprises a closed circuit television (CCTV)system 112 and at least one POS terminal 114. Data acquired from theCCTV system 112 and the POS terminal 114 is fed to an in-store dataaggregator 116. The aggregator 116 collates all video and POS datarelating to transactions. Additionally, or alternatively, the aggregator116 collects data relating to stocking levels of shelves, EPL and/orRFID data relating to prices and sales of goods. In at least oneembodiment, the correlation of video data from areas of the store 102along any one or combination of stocking levels, EPL or RFID data allowsfor the interdependence of sales and store layout to be monitored bysubsequent data processing of this data for correlations.

Each data analysis module 108 a-c comprises a transceiver 117, aprocessor 118 and a rules database 120. The processor 118 of at leastone of the data analysis module 108 a-c runs a respective store retailmanager agent 122 a-d for each store 102 a-d, and data analysisapplication 124. Additionally, or alternatively, the processor 118 runsa machine-learning algorithm 126. It will be appreciated that in someembodiments the rules database 120 resident on each data analysis module108 a-c may be a direct copy of that present on at least one other dataanalysis module, or it may be tailored for a particular aspect of dataanalysis.

It will be appreciated that there is typically one retail managementagent 122 a-d for each store and this may be located on one of the dataanalysis modules 108 a-c that controls the processing of the aggregateddata across the data analysis modules 108 a-c. Alternatively, the retailmanagement agent 122 a-d can be distributed across the data analysismodules 108 a-c.

In use, the aggregated data is received at one of the data analysismodules 108 a-c where the retail management agent 122 assigns parts orall of the aggregated data to the data analysis modules 108 a-c foranalysis. In at least one embodiment, the machine learning algorithm 126analyses the aggregated data for any previously unknown patterns withinthe data, or for patterns that deviate slightly from those alreadydefined in the rules database 120. The machine learning algorithm 126records these data patterns for incorporation into the rules database120, should the data pattern be identified as corresponding to an eventthat is to be monitored in the future.

The processor 118 runs an incident analysis routine that analyses thecollated POS and CCTV data in order to establish patterns thatcorrespond to an incident. Typically, the incident analysis routine is avideo content analysis routine. In one non-limiting example, the changein movement of a scanned item associated with a “sweethearting” withinCCTV data can be cross-referenced with the scan of a low value item at aPOS to determine that an event of “sweethearting” is likely to occur.The rules database 120 is accessed during this analysis such that anynumber of models of stored event types can be compared to the data toprovide a rich analysis of the data beyond merely identifying“sweethearting”.

In at least one embodiment, the retail management agent 122 is providedwith data structures which details for example any of the following themanagement structure, staff rosters, layout, stock levels and historicalsales data of each store 102 a-d. This allows, for example, an analysisto be carried out as to which employees are present when an event occursand/or which areas of the store 102 a-d are most susceptible to stockloss etc.

The above detailed usage of the system provides an overview of thesituation of a single store. However, in many retail operations theestate extends over a multiplicity of store locations, for example thefour stores of FIG. 1 may be divided into two regions. The processor 118receives processed data relating to each store 102 a-d from itsrespective retail management agent 122 and runs an intelligentorganisational modelling (IOM) routine in relation to the processeddata. The IOM routine collates all of the processed data to establishpatterns within it, for example the stores 102 a,b which form Region 1may show a high incidence of “sweethearting”, whilst the stores 102 c,dwhich form Region 2 may not. However, for example, the stores of Region2 102 c,d may show high proportions of stock loss of alcoholic beverageswhere as the stores of Region 1 show none. Such regional, national oreven store level patterns can be established. The establishment of thesepatterns allows for their inclusion into further rules to be introducedinto the rules database once the cause of these patterns has beencorrelated to an action or type of incident. For example, inner citystores may be found to have a higher incidence of stock loss due totheft and rural stores may be more prone to “sweethearting”. Onceestablished as such the relative thresholds for flagging the twoincidents in the respective types of stores can be accurately andintelligently set by the software, and dynamically monitored and alteredby the software as more data becomes available over time to improve theaccuracy of detection.

Once the IOM routine has analysed the data it generates output alertsthat are to be sent to a data communication elevator (DCE) 128 which128,which is also resident upon the data analysis modules 108 a-c. The DCE128 contains a detailed breakdown of the retailer's management hierarchy130. The DCE 128 determines which level of management should be informedof an incident dependent upon, for example the severity of the incident.For example, a single instance of “sweethearting” may be deemed suitablefor reporting to a store manager 130 a, in order that they can deal withit. However, a repeated instance of stock loss from a storeroom may beconsidered suitable for reporting to a regional manager 130 b, as itcannot be guaranteed that a store duty manager, or overall manager wasnot complicit. In an extreme case, the DCE 128 may elevate an alertdirectly to the chief executive officer, or owner, 130 d of the retailgroup.

Furthermore, the DCE 128 actively retains historic data and comparesreal-time data with such historic data. This allows for trends inincidents to be established and for appropriate elevation or demotion ofthe level of management hierarchy 130 to which an alert is directed. Forexample, the failure to address an issue that is prevalent in a regionby a regional manager may be escalated to an operations manager 130 c.Conversely, where a regional manager 130 b was being sent alerts relatedto an issue within in his area alerts relating to this issue can bedemoted to local managers 130 a, where there are only localisedinstances of the issue occurring, indicating that the regional problemhas been adequately addressed.

Once the correct level of management hierarchy has been addressed analert is issued to the remote device associated with the managerconcerned via the transceiver 117 and a suitable network. For example,for a mobile telephone a GSM, CDMA or UTMS network can be employed andfor a laptop etc., the Internet and where appropriate a wirelessnetwork.

Referring now to FIG. 3, a method of managing a retail store virtuallycomprises acquiring event data indicative of an event within the retailstore 102 a-d from a CCTV system 112 and a POS terminal 114 (Step 300).The aggregated event data is received from an in-store data collectionat a data processing module 108 a-c via a network (Step 302). The eventdata is processed according to a set of pre-defined rules (Step 304). Analert is generated in response to the processing of the event dataindicating that a specified event has occurred at the data processingmodule 108 a-c (Step 306). The alert is transmitted to a remote deviceassociated with an employee associated with the retail store 102 a-d(Step 308).

In at least one embodiment, the method comprises escalating of the alertthrough a retailer's management hierarchy prior to its being sent.

It will be appreciated that although described with reference to “rules”the “rules” may be applied in the form of any of the following:threshold, frequency, and/or decision making algorithms.

It will be appreciated that the term “employee” as used herein isintended to encompass a business owner or any third party granted accessto the output of the virtual retail management system described herein.

Typically, each module comprises a processor to enable the module toperform its function, and a communications facility to enable the moduleto communicate with outside entities, but in some instances this may notbe essential.

It will also be appreciated that the steps of the methods describedherein may be carried out in any suitable order, or simultaneously whereappropriate. The methods described herein may be performed by softwarein machine-readable form on a tangible storage medium or as apropagating signal.

Various modifications may be made to the above described embodimentwithout departing from the spirit and the scope of the invention.

1-54. (canceled)
 55. A virtual management system data processing unitcomprising: at transceiver arranged to control the flow of data to andfrom the data processing unit; a processor arranged to receive, via thetransceiver at least a portion of event data acquired from at least onelocal data acquisition device; the processor being further arrangedprocess the event data according to a set of pre-defined rules, beingarranged to generate an alert in response to the processing of the eventdata indicating that a specified event has occurred, and being furtherarranged to transmit the alert to a remote device associated with anemployee, via the transceiver.
 56. The virtual management system dataprocessing unit according to claim 55 wherein the rules comprise atleast one escalation rule related to the escalation of the alert througha management hierarchy.
 57. The virtual management system dataprocessing unit according to claim 56, wherein the at least oneescalation rule is related to escalating the alert based upon at leastone of the following: a delay in entering a response to the alert at a,or the, remote device, an increased frequency of the event, thereoccurrence of the event.
 58. The virtual management system dataprocessing unit according to claim 56, wherein the at least oneescalation rule may be related to at least one of the following: aparticular retail unit, a group of retail units, a geographical area, aperson, a time period.
 59. The virtual management system data processingunit according to claim 55 wherein the rules are arranged to identifyclusters of events.
 60. The virtual management system data processingunit according to claim 59 wherein the clusters of events may begeographically linked, temporally linked and/or linked to one or morepersons.
 61. The virtual management system data processing unitaccording to claim 55 wherein the rules comprise dynamically variablerules.
 62. The virtual management system data processing unit accordingto claim 61 wherein the dynamically variable rules comprisemachine-learning algorithms.
 63. The virtual management system dataprocessing unit according to claim 55 wherein the processor is arrangedto update the rules in response to event data.
 64. The virtualmanagement system data processing unit according to claim 63 wherein theprocessor is arranged to compare an event identified by the rules tostored model event data.
 65. The virtual management system dataprocessing unit according to claim 64 wherein the processor is arrangedto update parameters associated with the model event data in response tothe comparison.
 66. The virtual management system data processing unitaccording to claim 65 wherein the processor is arranged to selectivelygenerate the alert based upon the comparison.
 67. The virtual managementsystem data processing unit according to claim 63 wherein the processorhas an instance of a virtual manager agent associated with a retailstore running thereupon, the virtual manager agent being arranged tocontrol the application of the rules and the generation of the alert.68. The virtual management system data processing unit according toclaim 67 wherein the virtual manager agent is arranged to control theescalation of the alert through a management hierarchy.
 69. The virtualmanagement system data processing unit according to claim 67 wherein thevirtual manager agent is run on the at least one data processing module,or is instantiated across a plurality of the data processing modules.70. A method of virtually managing stock comprising: acquiring eventdata indicative of an event at at least one local data acquisitiondevice; aggregating the event data at a local data collection device;receiving the aggregated event data from the data collection device viaa network; processing the event data according to a set of pre-definedrules; generating an alert in response to the processing of the eventdata indicating that a pre-defined event has occurred at the at leastone data processing module; and transmitting the alert to a remotedevice associated with an employee.
 71. The method of claim 70 furthercomprising escalating of the alert through a management hierarchy. 72.The method of claim 70 further comprising escalating the alert basedupon at least one of the following: a delay in entering a response tothe alert at a, or the, remote device, an increased frequency of theevent, the reoccurrence of the event.
 73. The method of claim 70 furthercomprising escalating the alert based upon at least one of thefollowing: a particular retail unit, a group of retail units, ageographical area, a person, a time period.
 74. The method according toclaim 70 further comprising identifying clusters of events.
 75. Themethod according to claim 74 wherein clusters are geographically linked,temporally linked and/or linked to one or more persons.
 76. The methodaccording to claim 70 further comprising varying the rules dynamically.77. The method according to claim 76 wherein the dynamically variablerules may comprise machine-learning algorithms.
 78. The method accordingto 70 comprising comparing an event identified by the rules to storedmodel event data.
 79. The method according to claim 78 furthercomprising updating parameters associated with the model event data inresponse to the comparison.
 80. The method according to claim 78comprising selectively generating the alert based upon the comparison.81. The method according to claim 70 further comprising identifying acorrelation between POS event data and video event data that correspondsto an indication of a fraudulent transaction.
 82. The method accordingto claim 70 further comprising identifying a correlation between POSevent data, video event data and input from at least one sensor and/orother input device that corresponds to an indication of a fraudulenttransaction.
 83. The method according to claim 70 comprisinginstantiating an instance of a virtual manager agent associated with aparticular retail store, the virtual manager agent being arranged tocontrol the application of the rules and the generation of the alert.84. The method according to claim 83 comprising controlling theescalation of the alert through a management hierarchy via the virtualmanager agent.
 85. The method according to claim 83 comprisinginstantiating a portion a virtual manager agent associated with aparticular retail store on the at least one data processing module. 86.A computer readable storage medium carrying a computer program storedthereon which when executed cause a processor to: acquire event dataindicative of an event at at least one local data acquisition device;aggregate the event data at a local data collection device; receive theaggregated event data from the data collection device via a network;process the event data according to a set of pre-defined rules; generatean alert in response to the processing of the event data indicating thata pre-defined event has occurred at the at least one data processingmodule; and transmit the alert to a remote device associated with anemployee.
 87. The computer readable storage medium of claim 86 furthercomprising escalating of the alert through a management hierarchy. 88.The computer readable storage medium of claim 86 further comprisingescalating the alert based upon at least one of the following: a delayin entering a response to the alert at the remote device, an increasedfrequency of the event, or the reoccurrence of the event.
 89. Thecomputer readable storage medium of claim 86 further comprisingescalating the alert based upon at least one of the following: aparticular retail unit, a group of retail units, a geographical area, aperson, a time period.
 90. The computer readable storage mediumaccording to claim 86 further comprising identifying clusters of events.91. The computer readable storage medium according to claim 90 whereinclusters are geographically linked, temporally linked and/or linked toone or more persons.
 92. The computer readable storage medium accordingto claim 86 further comprising varying the rules dynamically.
 93. Thecomputer readable storage medium according to claim 92 wherein thedynamically variable rules may comprise machine-learning algorithms. 94.The computer readable storage medium according to claim 86 comprisingcomparing an event identified by the rules to stored model event data.95. The computer readable storage medium according to claim 94 furthercomprising updating parameters associated with the model event data inresponse to the comparison.
 96. The computer readable storage mediumaccording to claim 94 comprising selectively generating the alert basedupon the comparison.
 97. The computer readable storage medium accordingto claim 86 further comprising identifying a correlation between POSevent data and video event data that corresponds to an indication of afraudulent transaction.
 98. The computer readable storage mediumaccording to claim 86 further comprising identifying a correlationbetween POS event data, video event data and input from at least onesensor and/or other input device that corresponds to an indication of afraudulent transaction.
 99. The computer readable storage mediumaccording to claim 86 comprising instantiating an instance of a virtualmanager agent associated with a particular retail store, the virtualmanager agent being arranged to control the application of the rules andthe generation of the alert.
 100. The computer readable storage mediumaccording to claim 99 comprising controlling the escalation of the alertthrough a management hierarchy via the virtual manager agent.
 101. Themethod according to claim 99 comprising instantiating a portion avirtual manager agent associated with a particular retail store on theat least one data processing module.
 102. A virtual management systemcomprising: at least one data acquisition device arranged to acquireevent data indicative of an event at a location; a local data collectiondevice arranged to aggregate the event data; at least one dataprocessing module arranged to receive the aggregated event data from thelocal data collection device via a network, and being further arrangedto process the event data according to a set of pre-defined rules; theat least one data processing module being further arranged to generatean alert in response to the processing of the event data indicating thata predefined event has occurred, and being further arranged to transmitthe alert to a remote device associated with an employee.
 103. Thevirtual management system according to claim 102 wherein the rulescomprise at least one escalation rule related to the escalation of thealert through a management hierarchy.
 104. The virtual management systemaccording to claim 102 wherein the at least one escalation rule isrelated to escalating the alert based upon at least one of thefollowing: a delay in entering a response to the alert at the remotedevice an increased frequency of the event, or the reoccurrence of theevent.
 105. The virtual management system according to claim 102 whereinthe at least one escalation rule is related to at least one of thefollowing: a particular retail unit, area within a retail unit, a groupof retail units, a geographical area, a person, group of persons, arelationship between persons, a time period.
 106. The virtual managementsystem according to claim 102 wherein the rules are arranged to identifyclusters of events.
 107. The virtual management system according toclaim 106 wherein the clusters are geographically linked, temporallylinked, technologically linked, and/or linked to one or more persons,and/or one or more business factors said business factors selected froma list including footfall, spend per customer, customer satisfaction,type of goods purchased, queue management, merchandising management, hotspot/cold spot metrics, customer theft identification, trolley andbasket loss identification, company rules and procedure violationidentification, cash room irregular activity identification, customeraccident identification, employee accident identification or in storesafety hazard identification.
 108. The virtual management systemaccording to claim 102 wherein the rules comprise dynamically variablerules.
 109. The virtual management system according to claim 108 whereinthe dynamically variable rules comprise machine-learning algorithms.110. The virtual management system according to claim 102 wherein the atleast one data processing module is arranged to compare an eventidentified by the rules to stored model event data.
 111. The virtualmanagement system according to claim 110 wherein the at least oneprocessing module may be arranged to update parameters associated withthe model event data in response to the comparison.
 112. The virtualmanagement system according to claim 110 wherein the at least oneprocessing module may be arranged to selectively generate the alertbased upon the comparison.
 113. The virtual management system accordingto claim 102 wherein the at least one data acquisition device comprisesat least one of the following: a POS terminal, a video camera, aradio-frequency identification, RFID tag, an electronic price label,EPL, a location sensor, an audio sensor, an accelerometer, amagnetometer, an electrometer, an electro-optical sensor, a tactilesensor, a piezoelectric sensor, a heat sensor, a proximity sensor, anyother suitable sensor for detecting any of the following: position,speed, heat, presence of an object, position, angle, distance,displacement, electrical field, electromagnetic field, gravitationalfield, force, density, direction, flow properties, for example but notlimited to, flow of people, chemical sensor, environmental sensor, forexample but not limited to weather sensor.
 114. The virtual managementsystem according to claim 102 wherein the at least one data processingmodule comprises a rule arranged to identify a correlation between POSphysical event data and video event data which corresponds to anindication of a fraudulent transaction.
 115. The virtual managementsystem according to claim 102 wherein the at least one data processingmodule comprises a rule arranged to identify a correlation between POSphysical event data, video event data and input from at least one sensorand/or other input device, which corresponds to an indication of afraudulent transaction.
 116. The virtual management system according toclaim 115 wherein the physical event data includes POS transaction data.117. The virtual management system according to claim 102 wherein thesystem comprises a plurality of data processing modules.
 118. Thevirtual management system according to claim 117 wherein the pluralityof data processing modules may be distributed geographically.
 119. Thevirtual management system according to claim 117 wherein differing dataprocessing modules are arranged to process different portions of theaggregated event data.
 120. The virtual management system according toclaim 119 wherein differing portions of the event data relate todiffering event types.
 121. The virtual management system according toclaim 102 wherein each location has an instance of a virtual manageragent associated with it, the virtual manager agent being arranged tocontrol the application of the rules and the generation of the alert.122. The virtual management system according to claim 121 wherein thevirtual manager agent is arranged to control the escalation of the alertthrough the management hierarchy.
 123. The virtual management systemaccording to claim 122 wherein the virtual manager agent is run on theat least one data processing module, or instantiated across a pluralityof the data processing modules.